Co-evolutionary Rule-Chaining Genetic Programming

Abstract. A novel Genetic Programming (GP) paradigm called Coevolutionary Rule-Chaining Genetic Programming (CRGP) has been proposed to learn the relationships among attributes represented by a set of classification rules for multi-class problems. It employs backward chaining inference to carry out classification based on the acquired acyclic rule set. Its main advantages are: 1) it can handle more than one class at a time; 2) it avoids cyclic result; 3) unlike Bayesian Network (BN), the CRGP can handle input attributes with continuous values directly; and 4) with the flexibility of GP, CRGP can learn complex relationship. We have demonstrated its better performance on one synthetic and one real-life medical data sets.

• Type: Conference
• Year: 2005
• Where: IDEAL
• Authors: Wing-Ho Shum, Kwong-Sak Leung, Man Leung Wong
• Keywords: Backward Chaining, Genetic Programming, IDEAL 2005, Rule-Chaining Genetic Programming